




from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
       import PIL

# soft dependency
if is_pytesseract_available():
       import pytesseract

SCREAMING_SNAKE_CASE__			: Union[str, Any]    =  logging.get_logger(__name__)

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE       )      ->					Union[str, Any]:
       return [
           int(1000 * (box[0] / width)       ),
           int(1000 * (box[1] / height)       ),
           int(1000 * (box[2] / width)       ),
           int(1000 * (box[3] / height)       ),
       ]

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE = None       )      ->					Optional[int]:
       lowerCamelCase  : Tuple     					=   tesseract_config if tesseract_config is not None else ""

       # apply OCR
       lowerCamelCase  : List[Any]     					=   to_pil_image(UpperCamelCase_       )
       lowerCamelCase					, lowerCamelCase  : List[str]     					=   pil_image.size
       lowerCamelCase  : int     					=   pytesseract.image_to_data(UpperCamelCase_					,lang=UpperCamelCase_					,output_type="dict"					,config=UpperCamelCase_       )
       lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase  : str     					=   data["text"], data["left"], data["top"], data["width"], data["height"]

       # filter empty words and corresponding coordinates
       lowerCamelCase  : List[str]     					=   [idx for idx, word in enumerate(UpperCamelCase_       ) if not word.strip()]
       lowerCamelCase  : Any     					=   [word for idx, word in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
       lowerCamelCase  : int     					=   [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
       lowerCamelCase  : List[str]     					=   [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
       lowerCamelCase  : str     					=   [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
       lowerCamelCase  : int     					=   [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       lowerCamelCase  : List[Any]     					=   []
       for x, y, w, h in zip(UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_       ):
              lowerCamelCase  : List[str]     					=   [x, y, x + w, y + h]
              actual_boxes.append(UpperCamelCase_       )

       # finally, normalize the bounding boxes
       lowerCamelCase  : Tuple     					=   []
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_       )       )

       assert len(UpperCamelCase_       ) == len(UpperCamelCase_       ), "Not as many words as there are bounding boxes"

       return words, normalized_boxes




class 						UpperCamelCase__      (__lowerCAmelCase	):

    '''simple docstring'''



    lowerCamelCase_ :      Optional[int]      	= ["""pixel_values"""]







    def __init__(       self	,    UpperCamelCase__ = True	,    UpperCamelCase__ = None	,    UpperCamelCase__ = PILImageResampling.BILINEAR	,    UpperCamelCase__ = True	,    UpperCamelCase__ = None	,    UpperCamelCase__ = ""	,    **UpperCamelCase__	,    )	-> Optional[Any]:
           super().__init__(**lowerCamelCase_	)
           lowerCamelCase  : List[Any]     					=   size if size is not None else {"height": 224, "width": 224}
           lowerCamelCase  : List[str]     					=   get_size_dict(lowerCamelCase_	)

           lowerCamelCase  : Any     					=   do_resize
           lowerCamelCase  : List[str]     					=   size
           lowerCamelCase  : List[str]     					=   resample
           lowerCamelCase  : Tuple     					=   apply_ocr
           lowerCamelCase  : int     					=   ocr_lang
           lowerCamelCase  : Any     					=   tesseract_config







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__ = PILImageResampling.BILINEAR	,    UpperCamelCase__ = None	,    **UpperCamelCase__	,    )	-> List[Any]:
           lowerCamelCase  : Optional[Any]     					=   get_size_dict(lowerCamelCase_	)
           if "height" not in size or "width" not in size:
                  raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''	)
           lowerCamelCase  : Dict     					=   (size["height"], size["width"])
           return resize(lowerCamelCase_	,    size=lowerCamelCase_	,    resample=lowerCamelCase_	,    data_format=lowerCamelCase_	,    **lowerCamelCase_	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = ChannelDimension.FIRST	,    **UpperCamelCase__	,    )	-> List[str]:
           lowerCamelCase  : Dict     					=   do_resize if do_resize is not None else self.do_resize
           lowerCamelCase  : List[Any]     					=   size if size is not None else self.size
           lowerCamelCase  : Any     					=   get_size_dict(lowerCamelCase_	)
           lowerCamelCase  : Dict     					=   resample if resample is not None else self.resample
           lowerCamelCase  : Union[str, Any]     					=   apply_ocr if apply_ocr is not None else self.apply_ocr
           lowerCamelCase  : List[str]     					=   ocr_lang if ocr_lang is not None else self.ocr_lang
           lowerCamelCase  : List[Any]     					=   tesseract_config if tesseract_config is not None else self.tesseract_config

           lowerCamelCase  : Optional[Any]     					=   make_list_of_images(lowerCamelCase_	)

           if not valid_images(lowerCamelCase_	):
                  raise ValueError(
                      "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                      "torch.Tensor, tf.Tensor or jax.ndarray."	)

           if do_resize and size is None:
                  raise ValueError("Size must be specified if do_resize is True."	)

           # All transformations expect numpy arrays.
           lowerCamelCase  : Optional[int]     					=   [to_numpy_array(lowerCamelCase_	) for image in images]

           if apply_ocr:
                  requires_backends(self	,    "pytesseract"	)
                  lowerCamelCase  : Any     					=   []
                  lowerCamelCase  : Union[str, Any]     					=   []
                  for image in images:
                         lowerCamelCase					, lowerCamelCase  : List[str]     					=   apply_tesseract(lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	)
                         words_batch.append(lowerCamelCase_	)
                         boxes_batch.append(lowerCamelCase_	)

           if do_resize:
                  lowerCamelCase  : Dict     					=   [self.resize(image=lowerCamelCase_	,    size=lowerCamelCase_	,    resample=lowerCamelCase_	) for image in images]

           # flip color channels from RGB to BGR (as Detectron2 requires this)
           lowerCamelCase  : str     					=   [flip_channel_order(lowerCamelCase_	) for image in images]
           lowerCamelCase  : List[str]     					=   [to_channel_dimension_format(lowerCamelCase_	,    lowerCamelCase_	) for image in images]

           lowerCamelCase  : Tuple     					=   BatchFeature(data={"pixel_values": images}	,    tensor_type=lowerCamelCase_	)

           if apply_ocr:
                  lowerCamelCase  : List[str]     					=   words_batch
                  lowerCamelCase  : Optional[Any]     					=   boxes_batch
           return data







import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_SCREAMING_SNAKE_CASE 		=      """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

_SCREAMING_SNAKE_CASE 		=      """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

_SCREAMING_SNAKE_CASE 		=      """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,     _KWARGS_DESCRIPTION		)
class SCREAMING_SNAKE_CASE_		(      datasets.Metric		):







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION   ,   citation=_CITATION   ,   inputs_description=_KWARGS_DESCRIPTION   ,   features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				)   ,   id="""references"""				),
            }				)   ,   codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,   reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]=4   ,   lowerCamelCase_						:						List[Any]=False				):





    """simple docstring"""


    UpperCamelCase			  =      compute_bleu(
        reference_corpus=lowerCamelCase_   ,   translation_corpus=lowerCamelCase_   ,   max_order=lowerCamelCase_   ,   smooth=lowerCamelCase_				)
    ((UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase))			  =      score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
